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相关概念视频

Prosopagnosia01:24

Prosopagnosia

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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Force Classification01:22

Force Classification

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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相关实验视频

Updated: Jul 3, 2025

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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伪标签协会和基于原型的不变学习,用于半监督的NIR-VIS面部识别.

Weipeng Hu, Yiming Yang, Haifeng Hu

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |February 15, 2024
    PubMed
    概括

    这项研究引入了近红外和可见 (NIR-VIS) 面部识别的半监督方法,减少了对广泛标记数据的需求. 这种新的方法实现了与监督方法可比的性能,即使没有身份标签.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 生物识别信息 生物识别信息

    背景情况:

    • 近红外和可见 (NIR-VIS) 面部识别严重依赖于大型标记数据集.
    • 获取和注释用于NIR-VIS人脸识别的跨域数据是昂贵和耗时的.
    • 现有的方法与NIR-VIS面部数据固有的域转移作斗争.

    研究的目的:

    • 为NIR-VIS异质人脸识别 (NIR-VIS-sHFR) 开发一种半监督的方法.
    • 为了应对跨领域面部识别任务中有限的标记数据的挑战.
    • 提出一种新的方法,可以学习可靠的表示,而不需要对所有数据进行明确的身份标签.

    主要方法:

    • 提出了一个新的伪标签协会和基于原型的不变学习 (LPL) 框架.
    • 实施跨域伪标签协会 (CLA) 进行代伪标签生成和跨域模型开发.
    • 引入了域内紧的表示学习 (ICR) 以在集群中进行特征分离和聚合.
    • 利用基于原型的域间不变学习 (PII) 来学习使用跨域原型的域内不变特征.

    主要成果:

    • 半监督的LPL方法实现了与监督学习方法相比的性能.
    • 证明有能力有效地学习强大的跨域表示.

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  • 展示了成功的NIR-VIS面部识别,即使没有完整的身份标签信息.
  • 在多个具有挑战性的NIR-VIS数据集上验证了方法.
  • 结论:

    • 拟议的LPL方法为NIR-VIS人脸识别提供了一种有效的半监督解决方案.
    • 它大大减少了对大量标记训练数据的依赖.
    • 该框架成功地学习了域不变特征,提高了跨不同域的识别稳定性.